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Dissimilarity-based classification of chromatographic profiles

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Abstract

This paper proposes a non-parametric method for the classification of thin-layer chromatographic (TLC) images from patterns represented in a dissimilarity space. Each pattern corresponds to a mixture of Gaussian approximation of the intensity profile. The methodology comprises various phases, including image processing and analysis steps to extract the chromatographic profiles and a classification phase to discriminate among two groups, one corresponding to normal cases and the other to three pathological classes. We present an extensive study of several dissimilarity-based approaches analysing the influence of the dissimilarity measure and the prototype selection method on the classification performance. The main conclusions of this paper are that, Match and Profile-difference dissimilarity measures present better results, and a new prototype selection methodology achieves a performance similar or even better than conventional methods. Furthermore, we also concluded that simplest classifiers, such as k-NN and linear discriminant classifiers (LDCs), present good performance being the overall classification error less than 10% for the four-class problem.

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Abbreviations

TLC:

Thin-layer chromatography

MoG:

Mixture of Gaussians

LDS:

Lysosomal storage disorders

k-NN:

k-Nearest neighbour

SIMCA:

Soft independent modelling of class analogy

LDA:

Linear discriminant analysis

WMDP:

Within-class most dissimilar prototype

WLDP:

Within-class less dissimilar prototype

BMDP:

Between-class most dissimilar prototype

BLDP:

Between-class less dissimilar prototype

ROI:

Image region of interest

ML:

Maximum likelihood

EM:

Expectation–maximization

GM1:

Gangliosidosis

HUR:

Hurler disorder

MAN:

Mannosidosis

LDC:

Linear discriminant classifier

QDC:

Quadratic discriminant classifier

SVM:

Support vector machines

LOO:

Leave-one-out

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Authors and Affiliations

  1. Instituto Superior de Engenharia do Porto, Rua Dr. António Bernardino Almeida, 431, 4200-072, Porto, Portugal

    António V. Sousa

  2. Instituto de Engenharia Biomédica, Rua Dr. Roberto Frias, Edif. I – poente, 4200-465, Porto, Portugal

    António V. Sousa, Ana Maria Mendonça & Aurélio Campilho

  3. Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465, Porto, Portugal

    Ana Maria Mendonça & Aurélio Campilho

Authors
  1. António V. Sousa
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  2. Ana Maria Mendonça
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  3. Aurélio Campilho
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Correspondence to António V. Sousa.

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Sousa, A.V., Mendonça, A.M. & Campilho, A. Dissimilarity-based classification of chromatographic profiles. Pattern Anal Applic 11, 409–423 (2008). https://doi.org/10.1007/s10044-008-0113-2

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